{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45111406-ed47-49b8-ade4-22877cb3313d",
   "metadata": {},
   "source": [
    "# NMF Topic Modeling\n",
    "\n",
    "* Author: \t\t\tValentina Gonzalez Rostani\n",
    "*  Contact mag384@pitt.edu\n",
    "* Date: August 19, 2024\n",
    "* Version: Python 3.8.17\n",
    "\n",
    "This jupyter notebook:\r\n",
    "\n",
    "- Createsinputs for Table A17: NMF Topic Modeling.\n",
    "- First it does the NMF for the US, then for Germany.\n",
    "- Once topic are created the proportion of each one of them is calculated. \n",
    "\n",
    "\n",
    "Input:\n",
    "\n",
    "- Data/cleaned_data.csv # Data for the US\n",
    "- Data/cleaned_data_G.csv # Data for Germany\n",
    "\n",
    "\n",
    "Output:\n",
    "\n",
    "- Table A17: NMF Topic Modeling, 4 clusters, top-10 terms. \t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac569280-70c8-406f-895f-d1e8cd49f022",
   "metadata": {},
   "source": [
    "### Defining directory and importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f31c926f-8e9c-4574-b440-0894488142c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import NMF\n",
    "import os\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fce6cdd-4413-4471-8615-a2c7849f42c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Get the current working directory (where the Jupyter notebook is located)\n",
    "current_directory = os.getcwd()\n",
    "\n",
    "# Move one level up\n",
    "parent_directory = os.path.dirname(current_directory)\n",
    "\n",
    "# Change the working directory to the parent directory\n",
    "os.chdir(parent_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71ef513-68f9-462a-8875-a935ddd8038e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Table A17 US part based on Trump Speeches\n",
    "\n",
    "### Loading data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43473830-18ea-45dc-8843-85fd81f2a1e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgonz\\Dropbox\\Pitt\\OneDrive for Business\\Dissertation - Vale\\Paper 2 - Political-Economic Polarization\\Replication\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(os.getcwd())\n",
    "# Read data into papers\n",
    "papers = pd.read_csv(\"Data/filtered_papers.csv\")\n",
    "\n",
    "papers.head()\n",
    "papers['text_new'] = papers['clean_text']\n",
    "\n",
    "papers = papers[['text_new']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29c6854b-8668-4bcc-a888-1e55015a961f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# regular expression to remove any punctuation, and then lowercase the text\n",
    "# Load the regular expression library\n",
    "import re\n",
    "# Remove punctuation\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: str(x))\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: re.sub('[,\\.!?]', '', x))\n",
    "# Convert the titles to lowercase\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: x.lower())\n",
    "\n",
    "# Print out the first rows of papers\n",
    "papers['text_new'].head()\n",
    "\n",
    "data = papers.text_new.values.tolist()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2e21c6f-06a6-4730-993c-11733104ecc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 157)\t0.2541362143925171\n",
      "  (0, 121)\t0.267210401562597\n",
      "  (0, 236)\t0.30608282861712227\n",
      "  (0, 84)\t0.29084662966495983\n",
      "  (0, 88)\t0.1605679036774965\n",
      "  (0, 160)\t0.3095009135181034\n",
      "  (0, 129)\t0.5752716648835042\n",
      "  (0, 230)\t0.19405504492725587\n",
      "  (0, 266)\t0.1965061630929149\n",
      "  (0, 357)\t0.31986792490495297\n",
      "  (0, 70)\t0.1693961300065119\n",
      "  (0, 193)\t0.16106520248676895\n",
      "  (1, 88)\t0.39752145846865616\n",
      "  (1, 129)\t0.7121062989633848\n",
      "  (1, 70)\t0.41937769078922194\n",
      "  (1, 193)\t0.3987526319686463\n",
      "  (2, 397)\t0.22335220056649369\n",
      "  (2, 29)\t0.2833958367387063\n",
      "  (2, 164)\t0.2935236635176798\n",
      "  (2, 30)\t0.28116618040007585\n",
      "  (2, 348)\t0.28455080751613276\n",
      "  (2, 306)\t0.2639623125653235\n",
      "  (2, 192)\t0.28694775536127126\n",
      "  (2, 85)\t0.253725926356122\n",
      "  (2, 54)\t0.44670440113298737\n",
      "  :\t:\n",
      "  (5705, 168)\t0.4111077452580616\n",
      "  (5705, 401)\t0.4288943921794285\n",
      "  (5705, 11)\t0.4370577993302784\n",
      "  (5705, 207)\t0.409901016420968\n",
      "  (5705, 24)\t0.23426900776262233\n",
      "  (5705, 487)\t0.36940383717648423\n",
      "  (5705, 319)\t0.21456556558036902\n",
      "  (5705, 88)\t0.2249935867439003\n",
      "  (5706, 277)\t0.8837739427797058\n",
      "  (5706, 319)\t0.46791411398205685\n",
      "  (5707, 474)\t0.75628101963952\n",
      "  (5707, 23)\t0.4544158311469677\n",
      "  (5707, 266)\t0.47068595871984387\n",
      "  (5708, 408)\t0.7223914720045738\n",
      "  (5708, 23)\t0.48027956413645023\n",
      "  (5708, 266)\t0.4974757295064418\n",
      "  (5709, 372)\t0.678826415933238\n",
      "  (5709, 23)\t0.5100169139414134\n",
      "  (5709, 266)\t0.5282778099872372\n",
      "  (5710, 175)\t0.5768138678764868\n",
      "  (5710, 23)\t0.5673717421389085\n",
      "  (5710, 266)\t0.5876861986193199\n",
      "  (5711, 44)\t0.6547316087516484\n",
      "  (5711, 426)\t0.46965600917769434\n",
      "  (5711, 168)\t0.5922412967235229\n",
      "X =  ['10' '100' '15' '20' '30' '33000' '35' '50' '8th' 'able' 'access'\n",
      " 'accomplish' 'according' 'actually' 'administration' 'african' 'agenda'\n",
      " 'ago' 'air' 'allow' 'allowed' 'amazing' 'amendment' 'america' 'american'\n",
      " 'americans' 'americas' 'anybody' 'anymore' 'appoint' 'ask' 'asking'\n",
      " 'attack' 'away' 'bad' 'badly' 'beautiful' 'beginning' 'believe' 'best'\n",
      " 'better' 'big' 'biggest' 'billion' 'bless' 'border' 'borders' 'break'\n",
      " 'bring' 'build' 'business' 'businesses' 'called' 'came' 'campaign' 'care'\n",
      " 'carolina' 'cash' 'chance' 'change' 'chicago' 'child' 'childcare'\n",
      " 'children' 'china' 'choice' 'cities' 'citizens' 'city' 'class' 'clinton'\n",
      " 'clintons' 'close' 'coal' 'come' 'coming' 'common' 'communities'\n",
      " 'community' 'companies' 'congress' 'constitution' 'control' 'core'\n",
      " 'corrupt' 'corruption' 'cost' 'countries' 'country' 'court' 'create'\n",
      " 'crime' 'criminal' 'cut' 'dangerous' 'day' 'days' 'dc' 'deal' 'deals'\n",
      " 'death' 'debate' 'debt' 'decades' 'defend' 'defense' 'deficit'\n",
      " 'democratic' 'democrats' 'department' 'detroit' 'did' 'didnt' 'different'\n",
      " 'disaster' 'disastrous' 'does' 'doesnt' 'doing' 'dollars' 'donald'\n",
      " 'donors' 'dont' 'dream' 'drugs' 'east' 'economic' 'economy' 'education'\n",
      " 'election' 'eliminate' 'emails' 'end' 'energy' 'enforcement' 'ensure'\n",
      " 'entire' 'especially' 'establishment' 'everybody' 'fact' 'factories'\n",
      " 'failed' 'failing' 'failures' 'families' 'family' 'far' 'fbi' 'federal'\n",
      " 'fight' 'fighting' 'fix' 'florida' 'folks' 'follow' 'force' 'foreign'\n",
      " 'forgotten' 'foundation' 'free' 'friends' 'future' 'gave' 'general'\n",
      " 'gets' 'getting' 'given' 'god' 'going' 'gone' 'gonna' 'good' 'got'\n",
      " 'government' 'great' 'greatest' 'growth' 'guy' 'half' 'hampshire'\n",
      " 'happen' 'happened' 'happening' 'hard' 'health' 'hear' 'heard' 'help'\n",
      " 'heres' 'hes' 'high' 'highest' 'hillary' 'hillarys' 'hispanic' 'history'\n",
      " 'home' 'honor' 'hope' 'horrible' 'house' 'hundreds' 'id' 'ill' 'illegal'\n",
      " 'im' 'imagine' 'immediately' 'immigrant' 'immigrants' 'immigration'\n",
      " 'important' 'includes' 'including' 'income' 'increase' 'incredible'\n",
      " 'infrastructure' 'inner' 'instead' 'interests' 'iran' 'iraq' 'isis'\n",
      " 'islamic' 'issue' 'ive' 'job' 'jobs' 'just' 'justice' 'justices' 'killed'\n",
      " 'killing' 'know' 'large' 'law' 'laws' 'leaders' 'leadership' 'leave'\n",
      " 'leaving' 'left' 'let' 'lets' 'libya' 'lie' 'lied' 'lies' 'life' 'like'\n",
      " 'live' 'lives' 'living' 'lobbyists' 'long' 'look' 'lose' 'lost' 'lot'\n",
      " 'love' 'low' 'lower' 'mails' 'major' 'make' 'making' 'man'\n",
      " 'manufacturing' 'massive' 'maybe' 'mean' 'means' 'media' 'men' 'mexico'\n",
      " 'michigan' 'middle' 'military' 'million' 'millions' 'money' 'movement'\n",
      " 'nafta' 'nation' 'national' 'nearly' 'need' 'new' 'nice' 'north'\n",
      " 'november' 'number' 'numbers' 'obama' 'obamacare' 'office' 'officers'\n",
      " 'oh' 'ohio' 'ok' 'old' 'open' 'opponent' 'opportunity' 'order'\n",
      " 'organization' 'outside' 'overseas' 'paid' 'parents' 'partnership'\n",
      " 'party' 'past' 'pay' 'paying' 'peace' 'pennsylvania' 'people' 'percent'\n",
      " 'person' 'place' 'places' 'plan' 'play' 'police' 'policies' 'policy'\n",
      " 'political' 'politicians' 'poverty' 'power' 'powerful' 'president'\n",
      " 'private' 'probably' 'problem' 'problems' 'prosperity' 'protect' 'proud'\n",
      " 'provide' 'public' 'radical' 'rate' 'real' 'really' 'reason' 'rebuild'\n",
      " 'reduce' 'reform' 'reforms' 'refugee' 'refugees' 'regulation'\n",
      " 'regulations' 'released' 'remember' 'renegotiate' 'repeal' 'replace'\n",
      " 'respect' 'rich' 'rid' 'rigged' 'right' 'roads' 'rules' 'run' 'running'\n",
      " 'russia' 'safe' 'safety' 'said' 'save' 'saw' 'say' 'saying' 'says'\n",
      " 'school' 'schools' 'second' 'secretary' 'security' 'seen' 'send' 'serve'\n",
      " 'server' 'set' 'share' 'shes' 'shot' 'signed' 'single' 'small' 'society'\n",
      " 'special' 'spent' 'stand' 'start' 'started' 'state' 'states' 'steel'\n",
      " 'stop' 'story' 'street' 'strong' 'students' 'suffering' 'support'\n",
      " 'supported' 'supreme' 'sure' 'syria' 'taken' 'taking' 'talk' 'talking'\n",
      " 'tax' 'taxes' 'tell' 'terrible' 'terrorism' 'terrorists' 'thank' 'thats'\n",
      " 'theres' 'theyre' 'theyve' 'thing' 'things' 'think' 'thousands' 'time'\n",
      " 'times' 'tired' 'today' 'total' 'totally' 'tough' 'tpp' 'trade'\n",
      " 'tremendous' 'trillion' 'true' 'trump' 'trying' 'turn' 'unbelievable'\n",
      " 'understand' 'united' 'use' 'used' 'veterans' 'victims' 'victory'\n",
      " 'violence' 'violent' 'voice' 'vote' 'voter' 'voters' 'voting' 'wages'\n",
      " 'waiting' 'wall' 'want' 'wants' 'war' 'washington' 'way' 'wealth'\n",
      " 'wealthy' 'went' 'weve' 'whats' 'white' 'win' 'winning' 'women'\n",
      " 'wonderful' 'wont' 'words' 'work' 'workers' 'working' 'world' 'worse'\n",
      " 'worst' 'wouldnt' 'wrong' 'year' 'years' 'yesterday' 'york' 'young'\n",
      " 'youre' 'youve']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=500, min_df=5, stop_words='english')\n",
    "X = vectorizer.fit_transform(data)\n",
    "words = np.array(vectorizer.get_feature_names_out())\n",
    "\n",
    "print(X)\n",
    "print(\"X = \", words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd5f414-dc52-42cf-b572-52bc41a3746f",
   "metadata": {},
   "source": [
    "### 4 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f882d233-c6b5-45b2-accd-91e5d58d9de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1: dont,im,know,want,jobs,new,country,american,people,going\n",
      "Topic 2: world,better,proud,going,wealthy,strong,safe,great,make,america\n",
      "Topic 3: donors,trade,obama,emails,secretary,wants,state,clintons,clinton,hillary\n",
      "Topic 4: incredible,good,love,today,want,everybody,great,god,bless,thank\n"
     ]
    }
   ],
   "source": [
    "nmf = NMF(n_components=4, solver='mu', init='nndsvda')\n",
    "W = nmf.fit_transform(X)\n",
    "H = nmf.components_\n",
    "\n",
    "# Use W to get the most likely topic for each sentence\n",
    "most_likely_topic = W.argmax(axis=1)\n",
    "\n",
    "# Add most_likely_topic as a new column to the papers dataframe\n",
    "papers['most_likely_topic'] = most_likely_topic\n",
    "\n",
    "\n",
    "for i, topic in enumerate(H):\n",
    "     print(\"Topic {}: {}\".format(i + 1, \",\".join([str(x) for x in words[topic.argsort()[-10:]]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddae3b36-60d0-4972-8975-8f2aa375bc77",
   "metadata": {},
   "source": [
    "### Topics proportions\n",
    "This method calculates the proportion of each topic based on the sum of the weights (or contributions) for each topic across all documents. It gives a sense of how much each topic contributes to the overall data set based on the NMF model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0a9ad40-7b14-4795-85aa-ce0b967d1eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 5712 samples in 0.002s...\n",
      "[t-SNE] Computed neighbors for 5712 samples in 0.159s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 5712 / 5712\n",
      "[t-SNE] Mean sigma: 0.000000\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 62.137474\n",
      "[t-SNE] KL divergence after 300 iterations: 1.653182\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply t-SNE to reduce the dimensionality of the data\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(W)\n",
    "\n",
    "# Calculate the proportion of each topic\n",
    "topic_proportions = W.sum(axis=0) / W.sum()\n",
    "\n",
    "\n",
    "\n",
    "# Create a pie chart with the topic proportions\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.pie(topic_proportions, labels=['Topic {}'.format(i+1) for i in range(len(topic_proportions))],\n",
    "       autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax.set_title('Topic Proportions')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54c4da0-f2b4-48c9-a9fb-66a5c6e7e0bc",
   "metadata": {},
   "source": [
    "# Table A17 Geramany part based on AfD Manifesto\n",
    "\n",
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3635a49f-9349-4133-a69e-1abaa86097ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data into papers\n",
    "papers = pd.read_csv(\"Data/filtered_papers_G.csv\")\n",
    "\n",
    "# Print head\n",
    "papers.head()\n",
    "papers['text_new'] = papers['clean_text']\n",
    "\n",
    "papers = papers[['text_new']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fc93b62-c8cc-475a-be71-3df5da86443e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# regular expression to remove any punctuation, and then lowercase the text\n",
    "# Load the regular expression library\n",
    "import re\n",
    "# Remove punctuation\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: str(x))\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: re.sub('[,\\.!?]', '', x))\n",
    "# Convert the titles to lowercase\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: x.lower())\n",
    "\n",
    "# Print out the first rows of papers\n",
    "papers['text_new'].head()\n",
    "\n",
    "data = papers.text_new.values.tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e134d0a-6740-4c83-a3c7-c176ff06de50",
   "metadata": {},
   "source": [
    "### Vectorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d18b3bd4-3035-4061-9b94-6e319b37f21c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 93)\t0.07174913158727336\n",
      "  (0, 141)\t0.08517716772192677\n",
      "  (0, 192)\t0.08062221260594189\n",
      "  (0, 28)\t0.14992129008730995\n",
      "  (0, 112)\t0.07330763289579756\n",
      "  (0, 85)\t0.0786015587106631\n",
      "  (0, 75)\t0.11044404142329659\n",
      "  (0, 130)\t0.12344523816910023\n",
      "  (0, 90)\t0.0786015587106631\n",
      "  (0, 37)\t0.08280468507616007\n",
      "  (0, 21)\t0.07496064504365497\n",
      "  (0, 59)\t0.08280468507616007\n",
      "  (0, 179)\t0.06506553689091266\n",
      "  (0, 169)\t0.07496064504365497\n",
      "  (0, 124)\t0.08280468507616007\n",
      "  (0, 106)\t0.16560937015232013\n",
      "  (0, 34)\t0.1572031174213262\n",
      "  (0, 150)\t0.08517716772192677\n",
      "  (0, 74)\t0.0786015587106631\n",
      "  (0, 166)\t0.08280468507616007\n",
      "  (0, 175)\t0.06887633490716778\n",
      "  (0, 44)\t0.14992129008730995\n",
      "  (0, 35)\t0.08280468507616007\n",
      "  (0, 116)\t0.05522202071164829\n",
      "  (0, 23)\t0.08517716772192677\n",
      "  :\t:\n",
      "  (88, 72)\t0.08947727669259041\n",
      "  (88, 60)\t0.08624589213964215\n",
      "  (88, 108)\t0.2645719464032926\n",
      "  (88, 170)\t0.06802572502537824\n",
      "  (88, 102)\t0.07387435634840024\n",
      "  (88, 47)\t0.06595541571635179\n",
      "  (88, 73)\t0.048581700363100805\n",
      "  (89, 83)\t0.24648434231513133\n",
      "  (89, 153)\t0.22745594884055537\n",
      "  (89, 41)\t0.20762650348631298\n",
      "  (89, 177)\t0.24648434231513133\n",
      "  (89, 80)\t0.23961888951556226\n",
      "  (89, 11)\t0.23330328516019314\n",
      "  (89, 123)\t0.19546356281130609\n",
      "  (89, 121)\t0.19179299716085516\n",
      "  (89, 125)\t0.15980061113160587\n",
      "  (89, 152)\t0.4242729399030788\n",
      "  (89, 45)\t0.5451197078575462\n",
      "  (89, 108)\t0.21691993039053534\n",
      "  (89, 154)\t0.21691993039053534\n",
      "  (90, 2)\t0.3314975196010503\n",
      "  (90, 123)\t0.2918564098205376\n",
      "  (90, 7)\t0.16430966336553682\n",
      "  (90, 62)\t0.8637687613051982\n",
      "  (90, 73)\t0.17842391113022094\n",
      "X =  ['able' 'according' 'account' 'act' 'addition' 'advocate' 'advocates'\n",
      " 'afd' 'aim' 'allow' 'appropriate' 'areas' 'authorities' 'based' 'basic'\n",
      " 'basis' 'better' 'care' 'case' 'central' 'chapter' 'children' 'citizens'\n",
      " 'civil' 'committed' 'common' 'comprehensive' 'conditions' 'constitution'\n",
      " 'constitutional' 'control' 'core' 'costs' 'countries' 'country' 'create'\n",
      " 'cultural' 'culture' 'current' 'currently' 'decision' 'demand' 'demands'\n",
      " 'democracy' 'democratic' 'development' 'does' 'economic' 'economy' 'end'\n",
      " 'ensure' 'environment' 'equal' 'established' 'eu' 'euro' 'europe'\n",
      " 'european' 'existing' 'families' 'family' 'favour' 'federal' 'financial'\n",
      " 'foreign' 'form' 'framework' 'free' 'freedom' 'fundamental' 'future'\n",
      " 'general' 'german' 'germany' 'good' 'government' 'high' 'immigration'\n",
      " 'income' 'increased' 'increasing' 'individual' 'influence'\n",
      " 'infrastructure' 'instead' 'institutions' 'integration' 'interests'\n",
      " 'international' 'labour' 'language' 'large' 'law' 'laws' 'lead' 'led'\n",
      " 'legal' 'legislation' 'level' 'life' 'limited' 'local' 'long' 'longer'\n",
      " 'low' 'lower' 'maintain' 'make' 'market' 'mass' 'means' 'measures'\n",
      " 'members' 'ment' 'migration' 'nation' 'national' 'nature' 'necessary'\n",
      " 'need' 'needs' 'new' 'non' 'number' 'open' 'order' 'parents' 'parliament'\n",
      " 'particular' 'parties' 'people' 'policies' 'policy' 'political'\n",
      " 'population' 'possible' 'power' 'prevent' 'principle' 'principles'\n",
      " 'private' 'protect' 'protection' 'public' 'quality' 'rates' 'reason'\n",
      " 'reasons' 'reduce' 'reduced' 'reform' 'regard' 'regulations' 'rejects'\n",
      " 'remain' 'required' 'requirements' 'respect' 'result' 'return' 'right'\n",
      " 'rights' 'role' 'schools' 'sector' 'security' 'self' 'services' 'set'\n",
      " 'shall' 'social' 'society' 'special' 'standards' 'state' 'states'\n",
      " 'strengthen' 'sufficient' 'supply' 'support' 'systems' 'taken' 'tax'\n",
      " 'term' 'time' 'tion' 'use' 'used' 'values' 'want' 'wants' 'way' 'welfare'\n",
      " 'whilst' 'years']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=1500, min_df=10, stop_words='english')\n",
    "X = vectorizer.fit_transform(data)\n",
    "words = np.array(vectorizer.get_feature_names_out())\n",
    "\n",
    "print(X)\n",
    "print(\"X = \", words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9255898d-22c0-4123-8db2-8aea8d37909e",
   "metadata": {},
   "source": [
    "### 4 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "684e2ade-a420-4762-a85b-2e0dce6439f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1: power,foreign,national,euro,policy,public,political,germany,afd,german\n",
      "Topic 2: foreign,culture,economy,nature,schools,infrastructure,protection,security,policy,chapter\n",
      "Topic 3: special,afd,social,support,schools,care,family,parents,families,children\n",
      "Topic 4: right,legal,germany,country,migration,social,eu,countries,integration,immigration\n"
     ]
    }
   ],
   "source": [
    "nmf = NMF(n_components=4, solver='mu', init='nndsvda')\n",
    "W = nmf.fit_transform(X)\n",
    "H = nmf.components_\n",
    "\n",
    "# Use W to get the most likely topic for each sentence\n",
    "most_likely_topic = W.argmax(axis=1)\n",
    "\n",
    "# Add most_likely_topic as a new column to the papers dataframe\n",
    "papers['most_likely_topic'] = most_likely_topic\n",
    "\n",
    "\n",
    "for i, topic in enumerate(H):\n",
    "     print(\"Topic {}: {}\".format(i + 1, \",\".join([str(x) for x in words[topic.argsort()[-10:]]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6e0b43-0b11-4da7-9572-fdc00e518201",
   "metadata": {},
   "source": [
    "### Topics proportions\n",
    "This method calculates the proportion of each topic based on the sum of the weights (or contributions) for each topic across all documents. It gives a sense of how much each topic contributes to the overall data set based on the NMF model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a57b509d-c02a-4292-85e0-adfcb9f02f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 90 nearest neighbors...\n",
      "[t-SNE] Indexed 91 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 91 samples in 0.110s...\n",
      "[t-SNE] Computed conditional probabilities for sample 91 / 91\n",
      "[t-SNE] Mean sigma: 0.087385\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 46.019737\n",
      "[t-SNE] KL divergence after 300 iterations: 0.097478\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply t-SNE to reduce the dimensionality of the data\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(W)\n",
    "\n",
    "# Calculate the proportion of each topic\n",
    "topic_proportions = W.sum(axis=0) / W.sum()\n",
    "\n",
    "\n",
    "\n",
    "# Create a pie chart with the topic proportions\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.pie(topic_proportions, labels=['Topic {}'.format(i+1) for i in range(len(topic_proportions))],\n",
    "       autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax.set_title('Topic Proportions')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
